This exercise
illustrates correlation, aggregation, and 2-Way Mixed Model (Between/Within)
ANOVA. It assumes prior familiarity with the basics of Factorial ANOVA. It uses
the PERS dataset, consisting of 90 cases and 968 variables. The variables represent
measures of traits and relevant behaviors for the dimensions of extraversion
(outgoingness) and conscientiousness, reported each week for three weeks by
a group of undergraduate psychology students.

If you could
measure the same behaviors for a group of people in two different situations,
how consistent do you think it would be? Do you think that your results would
be different if you repeated the measurements again the following week? How
much do you think human behavior is a reflection of personality characteristics,
situational constraints, or variations over time?

This exercise
illustrates the comparison of the same behaviors across two different situations,
using both a correlational and ANOVA approach. The overarching question addressed
by the PERS data is "How consistent is human behavior, and is the consistency
predictable from human personality?" Over the course of the prior exercises,
this question has been examined by intercorrelating different behaviors which
presumably are products of the same personality trait, and by using personality
trait measures to predict various relevant behaviors. The inherent unreliability
of behaviors measured on a single occasion was remedied by aggregating across
time (see Chapters 2, 3, 4). The narrow specificity of individual behaviors
was broadened by aggregation across behaviors (see Chapters 5, 6), creating
a behavioral measure comparable in generality to a personality trait scale.
This current exercise takes a different approach to the consistency question,
while incorporating the processes of temporal and content aggregation employed
successfully in the previous exercises. This exercise examines directly the
question "Is behavior consistent across different situational contexts?" This
question has been historically at the very heart of a psychological debate known
as the "person/situation controversy," (see Epstein & O’Brien, 1985, for
a brief history) which seeks to understand how much of our behavior is a function
of internal factors (e.g., personality), and how much is a function of external
factors (e.g., the particular situational context at the time). To examine this
requires measuring the same behaviors in at least two different situational
contexts. Further, given the unreliability of single-occasion measures of behavior,
the measures would need to be taken over several occasions.

The PERS dataset
includes cross-situational measures as described above: the same several behaviors
measured in each of two different, specific situations, repeated on the same
day each week for three weeks. For outgoingness, several behaviors related to
(a) an in-person conversation and (b) a telephone conversation, were measured
each week. For conscientiousness, several behaviors related to (a) the participant’s
most important class and (b) the participant’s least important class, were measured
each week.

Several methodological
and definitional questions may be raised about these measures. For example,
it surely is not the same conversations which are being reported on each week,
so how do these represent the same situations measured repeatedly over several
occasions? When viewed in this manner, of course they are not. But then, every
situation in our lives from moment to moment would be treated as different,
and the issue of cross-situational consistency would be meaningless. Insofar
as there is some continuity to our behavior when talking in person or on the
telephone, we can speak of these as similar "situations." We would certainly
expect variation over occasions on each of these, and that is why they are measured
repeatedly over time. The purist can avoid the issue by only using the data
for a single-week’s measurements. This is not advised, however (see Chapters
2, 3, 4). Another obvious question about the measures concerns the comparison
of the "most important" versus "least important" classes. This is not the same
as comparing "Psychology 1A" with "English Literature 5;" in fact, it means
that most participants are measured in different classes from each other. Aside
from the logistical difficulty (impossibility?) of finding two courses in common
for all the participants to compare, the problem is one of psychological comparability.
It seemed a clearer and more definitive situational contrast to use each student’s
own highest priority and lowest priority courses, rather than specific courses
which might not be perceived much differently by some or many students.

There are two
analytic strategies to use in comparing the behavior across situations: correlational,
or ANOVA. In either case, we will use the 3-week average measures (aggregates),
and we will analyze both the individual behaviors and a behavioral aggregate
(see Chapter 6). Examine the measures in the codebook in the section "Cross-situational
Measures (3-Week Average)." We will use the outgoingness measures in this exercise
(OSP1-OSP6; OST1-OST6). None of these variables need to be recoded, though the
OSP6 and OST6 may be too different from the others, and so we will not use them.
Create two new aggregate measures across the five behaviors:

osp5tot sum(osp1
to osp5)

ost5tot sum(ost1
to ost5)

The correlational
approach to comparing behavior cross-situationally is simple: correlate osp1
with ost1, osp2 with ost2, etc., including osp5tot with ost5tot. Look at your
results and explain them, given what you know about aggregation and the kinds
of correlations obtained in other exercises between behaviors and with personality
traits. You should find them to be lower than many of the other correlations
we examined in previous exercises. Why do you think that is? What do you think
would be the results if you used the measures for only Week#1 instead of the
3-week averages?

The ANOVA approach
is more complex, but it yields some interesting information about the interaction
between personality and situation influences, so it is favored by some researchers.
Recall from Chapter 9 that ANOVA compares conditions or groups, so we must divide
our participants into groups according to their personality characteristics.
This was done in Chapter 6; if you don’t still have the "extgrp" variable on
your datafile, go back to that exercise and recreate it. Our situations already
represent two "conditions" under which measurements were taken.

We will use
a "2-Way ANOVA." There are two "factors" in our analysis of variance: outgoingness
level (low/average/high) and situation (in-person/telephone conversation), and
so we have a 3x2 (or 2x3, if you prefer the reverse order) ANOVA. Since participants
are classified in only 1 of the 3 outgoingness groups, this is a "between-subjects"
factor. Since participants were measured in both situations, this is
a "within-subjects" factor. This particular analysis, then, is called a "mixed-model,"
or "between/within" ANOVA. We need to perform separate ANOVAs on each variable;
we will use the 5-item aggregate as an example here.

Since there
are repeated measures (situations; our "within-subjects" factor), we will use:

Analyze>General
Linear Model>Repeated Measures

Within-Subject
Factor Name: converse

Number of Levels:
2

(click on Add,
then on Define; you will get a new, larger dialog box)

Within-Subjects
Variable (converse): osp5tot, ost5tot

Between-Subjects
Factor(s): extgrp

Click on OK
to run the ANOVA

The output is
daunting, because the design is complex and SPSS provides many multivariate
statistics even when they are not needed. There are several tables of output;
we will look at the third table, called "Tests of Within-Subjects Effects."
This table gives 4 different computations for each of 3 different effects. We
will only examine the lines labeled "Sphericity Assumed." The F-ratio for Converse
is 12.945, and is highly statistically significant (.001 in the "Sig." Column
means less than a .001 probability of occuring by chance). The interaction effect
("Converse*Extgrp") is not statistically significant. Now move down to the last
table, "Tests of Between-Subjects Effects." The line for "Extgrp" shows that
it is statistically significant (F=8.034; probability less than .001 of occuring
by chance). So the analysis shows that there is significant variability in the
aggregated behaviors as a function of situational context (in-person versus
telephone conversation), and as a function of personality trait level (low versus
average versus high outgoingness). There does not appear to be any significant
interaction between the personality factors and the situational factors in influencing
behavior. Since significant effects were found, you would want to re-run the
analysis and include Options for descriptive statistics (giving you means for
the different conditions) and Post-hocs (giving you specific statistical comparisons
between conditions). Also, since this only analyzed the aggregated measures
(osp5tot & ost5tot), you will need to repeat the analysis for each of the
separate behaviors (e.g., osp1 & ost1) to compare to the correlations from
the first analysis.

Compare the
two analyses (correlational and ANOVA). Which do you prefer and why? You can
try the same analyses with the conscientiousness measures on your own.